Machine Learning and decision making – Part 2

In the second of a two part blog post, Marko Balabanovic, Chief Technology Officer at the Digital Catapult, writes about Machine Learning (ML) and the challenges around systems that make decisions.

This is the second part of a post about machine learning and decision making systems – read the first part if you haven’t already.

In the first part I gave a little background about where the field of machine learning is today, prompted by our recent participation in the The Royal Society’s investigation of machine learning. Following on from that, in this post I’ll claim that we have a bigger set of challenges around systems that make decisions, regardless of whether they use machine learning algorithms, or more conventional software.

So how do I decide whether to cede control to a machine learning system? I would say there are some quite different scenarios.

We can give agency to an algorithm to act directly on our behalf

We can give agency to an algorithm to directly act on our behalf. There’s a spectrum of risk. I can give my smart thermostat control over the temperature in my house. I can give my pension to a financial trading algorithm, or indeed a poker bot. As an Uber driver I can obey instructions from a central routing algorithm. The very visible ethical battleground currently is with autonomous cars and the trolley problem, but in fact these systems will become more pervasive and less visible than the robot cars soon to be seen on our high streets.

There are also algorithms whose decisions, using our personal data, directly affect us, but that aren’t visible to us or under our control. The medical diagnosis tool used by your doctor, the risk assessment algorithm used by your bank, and even the Facebook newsfeed that can affect our individual moods.

Toolkits for Machine Learning

And there are the fuzzier boundaries of this space, where algorithms make decisions that indirectly affect us, as a community or a population. A large group using the same navigation app, such as Waze, can all be re-routed.

Working with machine learning techniques is a rare skill among software engineers

As the toolkits for ML become more commonly used, we will see the work move from science to engineering. Currently working with machine learning techniques is a rare and valuable skill among software engineers, and typically requires higher-level training in computer science and statistics. The idea of algorithms that need to be trained, that learn and whose behaviour changes over time poses challenges to commonly-used workflows for developing, testing, deploying and maintaining software.

We expect that as “black box” machine learning libraries become more commonly used, with the corresponding easier and cheaper availability of bursts of cloud computing, they will integrate themselves into the day to day skills of a software engineer.

As the dominant designs emerge for architectures embedding machine learning within a broad range of software projects, and as the field of machine learning progresses, these decision making systems will improve, and so will be employed in more and more contexts. We believe that user control of personal data and what it’s used for is an important principle.

Which algorithms are making decisions about me?

The corresponding question with algorithms will be: what is making decisions about me, and can I understand how and why? This is a timely debate. Many popular machine learning methods will learn complex statistical models, or patterns of weights and connections in a neural network, whose resulting decisions will be very hard to understand in an individual case.

Rise of superintelligences

According to Wired Magazine, even within Google there has been reluctance to surrender control of search rankings to a learning system that is hard to adjust: With machine learning, he [Amit Singhal (Head of Google’s search engine ranking team)] wrote, the trouble was that “…it’s hard to explain and ascertain why a particular search result ranks more highly than another result for a given query”. He added: “It’s difficult to directly tweak a machine learning-based system to boost the importance of certain signals over others”.

There is concern about how we’ll cope with the rise of “superintelligences” (and indeed a new £10m centre announced to study the Future of Intelligence between the universities of Oxford, Cambridge, Berkley and Imperial College). However, we think the issues we’ve discussed will be upon us more quickly, with a vast number of “microintelligences” soaking into the software infrastructure powering our lives.

Although the recent advances in machine learning algorithms, particularly deep learning, are wonderful and astonishing, they are just part of the increasingly effective arsenal of data and decision making tools at our disposal. A better way to describe this new frontier is to talk about the move towards autonomous, decision making systems, algorithms that are becoming more embedded in our lives. As these algorithms move from their pure and innocent beginnings in research labs into the messy politics of the real world, we must remember that they are created for and by people in society. As Mark Rolston points out, it is up to us to develop human-centered solutions that resist corruption.